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Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB� codes for the examples are available on the book’s website.
New to the Second Edition
With more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises.
An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.
- Sales Rank: #429919 in Books
- Published on: 2011-10-26
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.50" w x 6.30" l, 2.55 pounds
- Binding: Hardcover
- 749 pages
Review
Praise for the First Edition
A nice feature of this book is that it makes the effort to explain the underlying principles behind the formula for each algorithm; the relationship between different algorithms is equally well addressed. … The text is a good combination of theory and practice. It will be a valuable addition to references for academic researchers and industrial engineers working in the field of estimation. It will also serve as a useful reference for graduate courses in control and estimation.
―AIAA Journal, Vol. 43, No. 1, January 2005
About the Author
John L. Crassidis, Ph.D., is a professor of mechanical and aerospace engineering and the associate director of the Center for Multisource Information Fusion at the University at Buffalo, State University of New York. He previously worked at Texas A&M University, the Catholic University of America, and NASA’s Goddard Space Flight Center, where he contributed to attitude determination and control schemes for numerous spacecraft missions.
John L. Junkins, Ph.D., is a distinguished professor of aerospace engineering and the founder and director of the Center for Mechanics and Control at Texas A&M University. In addition to his historical contributions in analytical dynamics and spacecraft GNC, Dr. Junkins and his team have designed, developed, and demonstrated several new electro-optical sensing technologies.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Excellent Chapters on Kalman Filtering
By N. Huff
I particularly enjoyed this book's introduction to the Kalman filter. Nothing I had ever read before could really give me a good "feel" for how the Kalman filter works and what it is actually doing when it is forming an estimate. The book's approach for introducing the KF is to first give a review of least mean squares estimation. Every engineering student (and a lot of students of other subjects) has used least mean squares. It is just basic curve fitting. It then goes on to describe weighted least mean squares curve fitting, which is just least mean squares with "weights" assigned to individual measurements based on their uncertainties. After that, the authors derive an algorithm for performing the curve fit as the measurements come in one at a time (sequentially), rather than using all the measurements at once as you do in weighted least mean squares. Well, that algorithm is the basic linear Kalman Filter. Approached from the direction that the authors use, it was very easy to understand. They also give very good introductions to the extended Kalman filter, and even the "unscented" Kalman filter (webpage tutorials on the UKF absolutely suck, the book has a much better dicussion on this topic). This book is now my primary reference text on Kalman fitering.
11 of 13 people found the following review helpful.
Outstanding inclusive text on estimation theory!
By Kim,Jong-Woo
It presents the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for navigation and tracking, vehicle attitude determination. There is a lot of material that is covered by this book. The examples are well presented and they really help you when working on the problems at the end of each chapter. Also, computer routines for all the examples shown in the text can be accessed. I have to say that this is an excellent book for estimation of dynamic systems.
7 of 8 people found the following review helpful.
Very readable, well written; requires strong math skills
By Brian Vandenberg
I'm a computer science & applied math graduate (undergrad) working as a computer scientist. I'm studying these and other topics in my spare time, not for grad school (at least, not yet).
I love this book, and I'm thoroughly impressed with how well it is written. It is the first book I've read with more than a brief treatment of calculus concepts in tandem with linear algebra (eg, derivatives with respect to a vector or matrix, differential equations involving matrix expressions, etc).
When reading some other books (eg, Haykin's Adaptive Filter Theory), I find myself staring blankly at pages, my thoughts drifting to other unrelated topics, and ultimately I have to re-read sections many times before a point sinks in -- even then, I feel as though some points are still eluding me.
In stark contrast to Haykin's book, Crassidis & Junkins do an excellent job of presenting concepts and briefly sketching proofs where necessary, while keeping the material interesting and approachable.
I'm tempted to drop a star because there are times while reading where one or more steps are left out, or a minor mistake leaves the observant reader to scratch their head for awhile before it becomes clear that a mistake was made -- eg, a T (for transpose) was left out of an expression -- but mistakes happen; just get the errata from the author's website, and hope you don't get stuck anywhere else not covered by the errata.
-Brian
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